Fault Tolerant Manual: Add loading to reload the states (#10699)

Co-authored-by: Kaushik B <45285388+kaushikb11@users.noreply.github.com>
Co-authored-by: Carlos Mocholi <carlossmocholi@gmail.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

CHANGELOG.md

@@ -18,7 +18,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
     * Add a `_rotate_worker_indices` utility to reload the state according the latest worker ([#10647](https://github.com/PyTorchLightning/pytorch-lightning/issues/10647))
     * Add stateful workers ([#10674](https://github.com/PyTorchLightning/pytorch-lightning/issues/10674))
     * Add an utility to collect the states across processes ([#10639](https://github.com/PyTorchLightning/pytorch-lightning/issues/10639))
-
+    * Add logic to reload the states across data loading components ([#10699](https://github.com/PyTorchLightning/pytorch-lightning/issues/10699))
 
 -
 

pytorch_lightning/loops/epoch/evaluation_epoch_loop.py

@@ -22,7 +22,7 @@ from deprecate import void
 from pytorch_lightning.loops.base import Loop
 from pytorch_lightning.loops.utilities import _update_dataloader_iter
 from pytorch_lightning.trainer.progress import BatchProgress
-from pytorch_lightning.utilities.auto_restart import MergedIteratorState, reload_dataloader_state_dict
+from pytorch_lightning.utilities.auto_restart import _reload_dataloader_state_dict, MergedIteratorState
 from pytorch_lightning.utilities.fetching import AbstractDataFetcher, DataFetcher
 from pytorch_lightning.utilities.model_helpers import is_overridden
 from pytorch_lightning.utilities.types import EPOCH_OUTPUT, STEP_OUTPUT
@@ -182,7 +182,7 @@ class EvaluationEpochLoop(Loop):
 
     def _reload_dataloader_state_dict(self, data_fetcher: AbstractDataFetcher):
         if not self.trainer.sanity_checking and self._dataloader_state_dict:
-            reload_dataloader_state_dict(data_fetcher.dataloader, self._dataloader_state_dict)
+            _reload_dataloader_state_dict(data_fetcher.dataloader, self._dataloader_state_dict)
             self._dataloader_state_dict = None
 
     def _num_completed_batches_reached(self) -> bool:

pytorch_lightning/trainer/supporters.py

@@ -24,9 +24,9 @@ from torch.utils.data.dataset import IterableDataset
 
 from pytorch_lightning.utilities.apply_func import apply_to_collection, apply_to_collections
 from pytorch_lightning.utilities.auto_restart import (
+    _reload_dataloader_state_dict,
     MergedIteratorState,
     patch_dataloader_iterator,
-    reload_dataloader_state_dict,
 )
 from pytorch_lightning.utilities.data import get_len
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
@@ -403,7 +403,7 @@ class CombinedLoader:
             if isinstance(dataloader, CycleIterator):
                 dataloader = dataloader_to_iter_on.loader
 
-            reload_dataloader_state_dict(dataloader, state_dict)
+            _reload_dataloader_state_dict(dataloader, state_dict)
 
             # We finally spawned the workers if any.
             it = iter(dataloader_to_iter_on)

pytorch_lightning/utilities/auto_restart.py

@@ -33,7 +33,6 @@ from typing_extensions import Protocol, runtime_checkable
 import pytorch_lightning as pl
 from pytorch_lightning.utilities.enums import _FaultTolerantMode, AutoRestartBatchKeys
 from pytorch_lightning.utilities.exceptions import MisconfigurationException
-from pytorch_lightning.utilities.imports import _fault_tolerant_training
 
 
 class FastForwardSampler(Sampler):
@@ -564,38 +563,90 @@ def _add_capture_metadata_collate(dataloader: DataLoader) -> None:
     )
 
 
-def reload_dataloader_state_dict(dataloader: DataLoader, state_dict: Dict[str, Any]) -> None:
-    """Utility to reload state_dict within dataloader for fault tolerance."""
+def _reload_dataloader_state_dict_automatic_map_dataset(dataloader: DataLoader, state_dict: Dict[str, Any]) -> None:
+    iterator_state = state_dict["state"][0]
 
-    if not _fault_tolerant_training():
-        return
+    if not isinstance(iterator_state, IteratorState):
+        iterator_state = IteratorState.from_state_dict(iterator_state)
 
+    # reload sampler state
+    ff_sampler = _find_fast_forward_samplers(dataloader)
+    ff_sampler.load_state_dict(iterator_state.sampler_state)
+
+    # reload dataset state
+    dataloader.dataset.load_state_dict(
+        iterator_state.dataset_state,
+        latest_worker_id=state_dict["latest_worker_id"],
+        num_workers=iterator_state.num_workers,
+    )
+
+
+def _reload_dataloader_state_dict_automatic_iterable_dataset(
+    dataset: CaptureIterableDataset, state_dict: Dict[str, Any]
+) -> None:
+    dataset.load_state_dict(
+        {sampler_name: state[0]["sampler_state"] for sampler_name, state in state_dict["state"].items()}
+    )
+
+
+def _reload_dataloader_state_dict_automatic(dataloader: DataLoader, state_dict: Dict[str, Any]) -> None:
     dataset = dataloader.dataset
-
     if isinstance(dataset, CaptureMapDataset):
-        iterator_state = state_dict["state"][0]
-
-        if not isinstance(iterator_state, IteratorState):
-            iterator_state = IteratorState.from_state_dict(iterator_state)
-
-        # reload sampler state
-        ff_sampler = _find_fast_forward_samplers(dataloader)
-        ff_sampler.load_state_dict(iterator_state.sampler_state)
-
-        # reload dataset state
-        dataset.load_state_dict(
-            iterator_state.dataset_state,
-            latest_worker_id=state_dict["latest_worker_id"],
-            num_workers=iterator_state.num_workers,
-        )
+        _reload_dataloader_state_dict_automatic_map_dataset(dataloader, state_dict)
 
     elif isinstance(dataset, CaptureIterableDataset):
-        dataset.load_state_dict(
-            {sampler_name: state[0]["sampler_state"] for sampler_name, state in state_dict["state"].items()}
-        )
+        _reload_dataloader_state_dict_automatic_iterable_dataset(dataset, state_dict)
 
     else:
-        raise MisconfigurationException("This shouldn't happen. Please, open an issue on PyTorch Lightning Github.")
+        raise MisconfigurationException("This shouldn't be happening. Please, open an issue.")
+
+
+def _reload_dataloader_state_dict_manual(dataloader: DataLoader, state_dict: Dict[str, Any]) -> None:
+    # In manual mode, we don't wrap the user objects with `CaptureMapDataset` or `CaptureIterableDataset`
+    # therefore, we need to reload the states manually.
+
+    latest_worker_id = state_dict["latest_worker_id"]
+    num_workers = state_dict["state"][latest_worker_id]["num_workers"]
+    sampler_state = state_dict["state"][latest_worker_id].get("sampler_state", None)
+    if sampler_state:
+        # `sampler_state` keys contain all the DataLoader attribute names
+        # which matched `_SupportsStateDict` API interface while collecting the `state_dict`.
+        for dataloader_attr_name in sampler_state:
+            obj = getattr(dataloader, dataloader_attr_name)
+            if not isinstance(obj, _SupportsStateDict):
+                raise MisconfigurationException(
+                    f"The DataLoader attribute {dataloader_attr_name}:{obj} should have a `load_state_dict` method."
+                )
+
+            obj.load_state_dict(sampler_state[dataloader_attr_name])
+
+    if not isinstance(dataloader.dataset, _SupportsStateDict):
+        return
+
+    dataset_state = {
+        worker_id: state_dict["state"][worker_id]["dataset_state"][worker_id]
+        for worker_id in state_dict["state"].keys()
+    }
+
+    dataloader.dataset.load_state_dict(_rotate_worker_indices(dataset_state, latest_worker_id, num_workers))
+
+
+def _reload_dataloader_state_dict(dataloader: DataLoader, state_dict: Dict[str, Any]) -> None:
+    """Utility to reload state_dict within dataloader for fault tolerance."""
+
+    fault_tolerant_mode = _FaultTolerantMode.detect_current_mode()
+
+    if not fault_tolerant_mode.is_enabled:
+        return
+
+    if fault_tolerant_mode.is_automatic:
+        _reload_dataloader_state_dict_automatic(dataloader, state_dict)
+
+    elif fault_tolerant_mode.is_manual:
+        _reload_dataloader_state_dict_manual(dataloader, state_dict)
+
+    else:
+        raise MisconfigurationException("This shouldn't be happening. Please, open an issue.")
 
 
 def _rotate_worker_indices(state: Dict[int, Any], latest_worker_id: int, num_workers: int) -> Dict[int, Any]:
@@ -638,7 +689,6 @@ class _StatefulDataLoaderIter:
             for k, v in self._loader.__dict__.items()
             if isinstance(v, _SupportsStateDict) and k != "dataset"
         }
-
         self.__accumulate_state(sampler_state)
 
     def _next_index(self) -> Any:

tests/utilities/test_auto_restart.py

@@ -19,6 +19,7 @@ from collections import defaultdict
 from collections.abc import Iterable
 from contextlib import suppress
 from copy import deepcopy
+from dataclasses import asdict
 from typing import List, Optional
 from unittest import mock
 from unittest.mock import ANY
@@ -42,6 +43,7 @@ from pytorch_lightning.utilities.auto_restart import (
     _dataloader_to_state_dict,
     _MultiProcessingDataLoaderIterStateful,
     _patch_dataloader_get_iterators,
+    _reload_dataloader_state_dict,
     _rotate_worker_indices,
     _SingleProcessDataLoaderIterStateful,
     _SupportsStateDict,
@@ -1289,7 +1291,7 @@ class StatefulRandomDataset(RandomDataset):
         return {"counter": self.counter}
 
     def load_state_dict(self, state_dict):
-        self.counter = state_dict["counter"]
+        self.counter = state_dict[0]["counter"]
 
 
 @pytest.mark.parametrize("num_workers", [0])
@@ -1319,7 +1321,9 @@ def test_stateful_workers(num_workers):
     assert isinstance(dataloader_iter, worker_type)
 
     next(data_fetcher_iter)
-    state = data_fetcher.dataloader_iter.state.state
+
+    reloaded_state = deepcopy(data_fetcher.dataloader_iter.state)
+    state = reloaded_state.state
     assert state[0].dataset_state == {0: {"counter": 1}}
     assert state[0].sampler_state["sampler"] == {"counter": 1}
 
@@ -1350,4 +1354,6 @@ def test_stateful_workers(num_workers):
     assert not hasattr(DataLoader, "_ori_get_iterator")
     assert DataLoader._get_iterator == _get_iterator_fn
 
+    _reload_dataloader_state_dict(dataloader, asdict(reloaded_state))
+    assert dataloader.sampler.counter == dataloader.dataset.counter == 1
     data_fetcher.teardown()